






'''simple docstring'''
import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


a_											=			logging.get_logger(__name__)

a_											=			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a_											=			{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
a_											=			{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
a_											=			{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

a_											=			{
    'facebook/dpr-ctx_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-ctx_encoder-multiset-base': 5_1_2,
}
a_											=			{
    'facebook/dpr-question_encoder-single-nq-base': 5_1_2,
    'facebook/dpr-question_encoder-multiset-base': 5_1_2,
}
a_											=			{
    'facebook/dpr-reader-single-nq-base': 5_1_2,
    'facebook/dpr-reader-multiset-base': 5_1_2,
}


a_											=			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
a_											=			{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
a_											=			{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}






class   __SCREAMING_SNAKE_CASE   (      _A					):
 snake_case_					=				VOCAB_FILES_NAMES
 snake_case_					=				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 snake_case_					=				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_					=				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
 snake_case_					=				DPRContextEncoderTokenizer






class   __SCREAMING_SNAKE_CASE   (      _A					):
 snake_case_					=				VOCAB_FILES_NAMES
 snake_case_					=				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 snake_case_					=				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_					=				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
 snake_case_					=				DPRQuestionEncoderTokenizer


a_											=			collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

a_											=			collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


a_											=			R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '






@add_start_docstrings(_A					)
class   __SCREAMING_SNAKE_CASE   :




 def __call__(   self						:				Tuple       ,   __lowercase						:				List[str]       ,   __lowercase						:				Optional[str] = None       ,   __lowercase						:				Optional[str] = None       ,   __lowercase						:				Union[bool, str] = False       ,   __lowercase						:				Union[bool, str] = False       ,   __lowercase						:				Optional[int] = None       ,   __lowercase						:				Optional[Union[str, TensorType]] = None       ,   __lowercase						:				Optional[bool] = None       ,   **__lowercase						:				List[str]       ,   )						->    BatchEncoding:






       if titles is None and texts is None:
             return super().__call__(
                 UpperCamelCase__       ,   padding=UpperCamelCase__       ,   truncation=UpperCamelCase__       ,   max_length=UpperCamelCase__       ,   return_tensors=UpperCamelCase__       ,   return_attention_mask=UpperCamelCase__       ,   **UpperCamelCase__       ,   )
       elif titles is None or texts is None:
             SCREAMING_SNAKE_CASE__  :					str						=titles if texts is None else texts
             return super().__call__(
                 UpperCamelCase__       ,   UpperCamelCase__       ,   padding=UpperCamelCase__       ,   truncation=UpperCamelCase__       ,   max_length=UpperCamelCase__       ,   return_tensors=UpperCamelCase__       ,   return_attention_mask=UpperCamelCase__       ,   **UpperCamelCase__       ,   )
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=titles if not isinstance(UpperCamelCase__       ,   UpperCamelCase__   ) else [titles]
       SCREAMING_SNAKE_CASE__  :					Dict						=texts if not isinstance(UpperCamelCase__       ,   UpperCamelCase__   ) else [texts]
       SCREAMING_SNAKE_CASE__  :					Dict						=len(UpperCamelCase__   )
       SCREAMING_SNAKE_CASE__  :					int						=questions if not isinstance(UpperCamelCase__       ,   UpperCamelCase__   ) else [questions] * n_passages
       assert len(UpperCamelCase__   ) == len(
           UpperCamelCase__   ), F"There should be as many titles than texts but got {len(UpperCamelCase__   )} titles and {len(UpperCamelCase__   )} texts."
       SCREAMING_SNAKE_CASE__  :					str						=super().__call__(UpperCamelCase__       ,   UpperCamelCase__       ,   padding=UpperCamelCase__       ,   truncation=UpperCamelCase__   )['''input_ids''']
       SCREAMING_SNAKE_CASE__  :					Tuple						=super().__call__(UpperCamelCase__       ,   add_special_tokens=UpperCamelCase__       ,   padding=UpperCamelCase__       ,   truncation=UpperCamelCase__   )['''input_ids''']
       SCREAMING_SNAKE_CASE__  :					Union[str, Any]						={
           '''input_ids''': [
               (encoded_question_and_title + encoded_text)[:max_length]
               if max_length is not None and truncation
               else encoded_question_and_title + encoded_text
               for encoded_question_and_title, encoded_text in zip(UpperCamelCase__       ,   UpperCamelCase__   )
           ]
       }
       if return_attention_mask is not False:
             SCREAMING_SNAKE_CASE__  :					Optional[int]						=[]
             for input_ids in encoded_inputs["input_ids"]:
                   attention_mask.append([int(input_id != self.pad_token_id   ) for input_id in input_ids]   )
             SCREAMING_SNAKE_CASE__  :					Any						=attention_mask
       return self.pad(UpperCamelCase__       ,   padding=UpperCamelCase__       ,   max_length=UpperCamelCase__       ,   return_tensors=UpperCamelCase__   )




 def   __magic_name__						(   self						:				List[Any]       ,   __lowercase						:				BatchEncoding       ,   __lowercase						:				DPRReaderOutput       ,   __lowercase						:				int = 16       ,   __lowercase						:				int = 64       ,   __lowercase						:				int = 4       ,   )						->    List[DPRSpanPrediction]:
       SCREAMING_SNAKE_CASE__  :					str						=reader_input['''input_ids''']
       SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__  :					Dict						=reader_output[:3]
       SCREAMING_SNAKE_CASE__  :					List[str]						=len(UpperCamelCase__   )
       SCREAMING_SNAKE_CASE__  :					List[str]						=sorted(range(UpperCamelCase__   )       ,   reverse=UpperCamelCase__       ,   key=relevance_logits.__getitem__   )
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=[]
       for doc_id in sorted_docs:
             SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=list(input_ids[doc_id]   )
             # assuming question & title information is at the beginning of the sequence
             SCREAMING_SNAKE_CASE__  :					str						=sequence_ids.index(self.sep_token_id       ,   2   ) + 1  # second sep id
             if sequence_ids[-1] == self.pad_token_id:
                   SCREAMING_SNAKE_CASE__  :					Any						=sequence_ids.index(self.pad_token_id   )
             else:
                   SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=len(UpperCamelCase__   )

             SCREAMING_SNAKE_CASE__  :					List[str]						=self._get_best_spans(
                 start_logits=start_logits[doc_id][passage_offset:sequence_len]       ,   end_logits=end_logits[doc_id][passage_offset:sequence_len]       ,   max_answer_length=UpperCamelCase__       ,   top_spans=UpperCamelCase__       ,   )
             for start_index, end_index in best_spans:
                   start_index += passage_offset
                   end_index += passage_offset
                   nbest_spans_predictions.append(
                       DPRSpanPrediction(
                           span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]       ,   relevance_score=relevance_logits[doc_id]       ,   doc_id=UpperCamelCase__       ,   start_index=UpperCamelCase__       ,   end_index=UpperCamelCase__       ,   text=self.decode(sequence_ids[start_index : end_index + 1]   )       ,   )   )
             if len(UpperCamelCase__   ) >= num_spans:
                   break
       return nbest_spans_predictions[:num_spans]




 def   __magic_name__						(   self						:				Optional[int]       ,   __lowercase						:				List[int]       ,   __lowercase						:				List[int]       ,   __lowercase						:				int       ,   __lowercase						:				int       ,   )						->    List[DPRSpanPrediction]:
       SCREAMING_SNAKE_CASE__  :					Dict						=[]
       for start_index, start_score in enumerate(UpperCamelCase__   ):
             for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]   ):
                   scores.append(((start_index, start_index + answer_length), start_score + end_score)   )
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=sorted(UpperCamelCase__       ,   key=lambda __lowercase   : x[1]       ,   reverse=UpperCamelCase__   )
       SCREAMING_SNAKE_CASE__  :					Optional[Any]						=[]
       for (start_index, end_index), score in scores:
             assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
             SCREAMING_SNAKE_CASE__  :					Optional[int]						=end_index - start_index + 1
             assert length <= max_answer_length, F"Span is too long: {length} > {max_answer_length}"
             if any(
                 start_index <= prev_start_index <= prev_end_index <= end_index
                 or prev_start_index <= start_index <= end_index <= prev_end_index
                 for (prev_start_index, prev_end_index) in chosen_span_intervals   ):
                   continue
             chosen_span_intervals.append((start_index, end_index)   )

             if len(UpperCamelCase__   ) == top_spans:
                   break
       return chosen_span_intervals






@add_end_docstrings(_A					)
class   __SCREAMING_SNAKE_CASE   (      _A						,					_A					):
 snake_case_					=				VOCAB_FILES_NAMES
 snake_case_					=				READER_PRETRAINED_VOCAB_FILES_MAP
 snake_case_					=				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_					=				READER_PRETRAINED_INIT_CONFIGURATION
 snake_case_					=				["""input_ids""", """attention_mask"""]
 snake_case_					=				DPRReaderTokenizer

import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  if gpta_config_file == "":
    __magic_name__								=			GPTaConfig()
  else:
    __magic_name__								=			GPTaConfig.from_json_file(A_   )
  __magic_name__								=			GPTaModel(A_   )

  # Load weights from numpy
  load_tf_weights_in_gpta(A_,	A_,	A_   )

  # Save pytorch-model
  __magic_name__								=			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __magic_name__								=			pytorch_dump_folder_path + """/""" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
  torch.save(model.state_dict(),	A_   )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(config.to_json_string()   )


if __name__ == "__main__":
    __lowerCAmelCase     :	List[str]       			=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--gpt2_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained OpenAI model. \n'
            'This specifies the model architecture.'
        ),
    )
    __lowerCAmelCase     :	Tuple       			=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)

